One of the most interesting topics that’s often discussed after a football match is whether a player was “involved” or not during a match. The concept makes intuitive sense to me. The oft-cited example of a player “dictating” or “influencing” a team’s possession is Andrea Pirlo. One doesn’t have to crunch data to understand that Pirlo was heavily involved in the possession of his AC Milan, Juventus, and Italy sides. However, as a data analyst, I was curious as to how we could measure this idea. Who are the most influential players to a team’s possession? Are there less obvious players to Pirlo that we’re missing? And how could one apply the measure of influence to help solve real world problems?

I decided to use graph databases to model team passing networks. From there, I borrowed a legendary algorithm from Silicoln Valley to measure influence, and ranked all players across the Top 5 leagues in Europe.

Note – this is not a direct measure of “how good” a player is. Rather, it is a measure of how involved a player is to a team’s possession.

Background: Graph Databases

Graph Databases are an alternative way to store data to the traditional data warehouse. In graph databases, nodes are entities that represent things. Edges represent links between things. Graph databases rose in popularity with the rise of social networks. Within a social network’s graph, each node represents a person, and each edge represents a relationship between people. For example, with Facebook, each Facebook profile is often graphed as a node, while each friendship is represented as an edge:

Social Graph

Graph databases can be applied to football. In a football match, each player is graphed as a node, while each combination of two players who pass to each other during the match is represented by an edge. Additionally, the size of the node is often displayed using a proxy of influence such as total passes or touches, while the size of the edge is weighted by the amount of passes between the two players. @11tegen11 has done great work to popularize this visualization. For example:

@11tegen‘s work inspired me to get into modeling matches as graphs. He does really interesting work and I recommend giving him a follow.

The Legendary PageRank

So how do we measure the influence of an individual player on a team’s possession using our graphs? Luckily, some really smart people have developed algorithms to measure influence within a graph. So, we can simply apply them to our passing networks.

Perhaps the most popular measure of connectivity is one that influences your decisions every day – PageRank. PageRank was originally developed by the founders of Google as a means to organize the internet. More specifically, it is meant to answer the question, what are the relative importances of the websites on the internet. The higher the PageRank, the higher a website returns in your search results. If you would like to know more details about the algorithm, there is plenty of good writing on the subject that I will not cover here today.

Applying PageRank to a football team’s passing network provides a similar insight. In this case, it tells us the relative importance of an individual to a team’s possession. The higher the score, the more involved the player is. This is not a measure of “who is the best.” This is a measure of involvement, or, influence on team while that team is in possession.

Methodology

Like my work on the classification of central midfielders, I limited my dataset to the last 18 months of player game level data from the Top 5 Leagues in Europe: English Premier League, Spanish La Liga, Italian Serie A, French League 1, and German Bundesliga. I included all positions in my analysis. I only deemed a player eligible if he had played the equivalent of 20 matches (1800 minutes) in the past 18 months.

I wrote a script that created a graph for each team in each match in the dataset. From there, I filtered each graph through PageRank. After sending the data through the algorithm, I had measures of influence on ball possession for each player in the dataset, for each match. From there, I simply took a player’s average measure of influence over the entire dataset, and ranked the players. Below are the Top 5 most influential players to their team’s possession in Europe over the past 18 months. I have included a passing map of their most influential match.

Results

5. Bruno, Villarreal: Captain Bruno, the central ball playing midfielder, was a key member of Villarreal’s 4th place finish under Marcelino last season. On the second to last match day, Bruno played alongside Manu Trigueros in a double pivot. On the day, most of the possession flowed through Bruno as he completed 94 percent of 104 passes in a disappointing 2-0 defeat to Deportivo.

Map: Villarreal 0 – Deportivo de La Coruña 2, May 8, 2016

4. Pascal Groß, Ingolstadt: Groß plays an influential role in Ingolstadt’s limited ball possession as what I would categorize a deep lying forward. When they do have the ball, it seems to be funneled straight throughGroß and teammate Tobias Levels (when Levels is in the starting 11). Groß played a key role in Ingolstadt’s 2-1 victory over Augsburg in February of last season. Groß only completed 66 percent of his 53 passes, but tallied 6 key passes. You can see how Groß played centrally behind the strikers in the map below.

Map: Ingolstadt 2 – Augsburg 1, February 6th, 2016

3. Daniel Drinkwater, Leicester City: Although Kante got the plaudits and the big money move to Chelsea, it was Drinkwater that dictated Leicester’s possession last season. Drinkwater has tallied 4 of the top 10 most influential passing matches in the Premier League over the last 18 months, including the top 3 most influential passing performances. Drinkwater’s most involved performance was in Leicester City’s 0-3 loss to Chelsea in October of this season. He drifted in front of his partner Daniel Amartey and completed 85 percent of his 102 passes.

Map: Leicester City 0 – Chelsea 3, October 15, 2016

2. Jorginho, Napoli: Central controller Jorginho had three of the top 10 most influential games in the Serie A over the last 18 months, including the most influential game in a 2-0 win over Verona. Jorginho played as a single pivot on the day, completed 93 percent of his astounding 195 passes, and provided 9 key passes. Performances like this make you wonder why clubs like Barcelona don’t sign Jorginho. He’s also an obvious heir-apparent to Cazorla at Arsenal (Cazorla also scores very high on PageRank).

Map: Napoli 2 – Verona 0, November 22, 2015

1. Roberto Trashorras, Rayo Vallecano: All of the top 10 single game PageRanks in La Liga over the last 18 months belonged to central possession midfielder Roberto Trashorras. The 35 year old king of influence is still playing for Rayo this season in La Liga 2. It is worth noting that the manager – Paco Jemez – with whom Trashorras worked with last season did not agree to terms with Rayo before the start of this season. On February 28th, 2016, Trashorras tallied the most influential performance in La Liga over the last 18 months in a 2-2 draw against Betis. He completed 87 percent of 100 passes, 17 of 20 long passes, and provided an assist.

Map: Rayo Vallecano 2 – Real Betis 2, February 28, 2016

Applications

Some of you may be thinking – so what? I think ‘so what’ is always an important question to keep in mind when working with data. It helps us remember if we’re actually answering a question that can lead to an actionable insight.

The most obvious application to applying PageRank to players as a means to measure influence is for opposition analysis. One look at Rayo Vallecano’s passing maps and PageRanks clearly shows that under Paco Jemez, all the possession went through Trashorras. Stop Trashorras, and you disrupt Rayo’s tactics. Similarly, you could evaluate the balance of a team by looking at PageRanks. For example, if a team’s PageRank scores are disproportionately heavy on the wings compared to the rest of Europe, the team uses more width in possession than an average team. Using a measure of influence like PageRank helps us understand the way a team likes to operate in possession, thus giving us the opportunity to disrupt.

Please feel free to add any comments/thoughts about the results or methodology.

Note – All pass networks other than @11tegen11’s network of Liverpool were created by me. @11tegen11’s sourced network of Liverpool was created by @11tegen11. All other non-sourced images were found using Google Advances Image Search option with image rights set to ‘free to user or share.’If Google’s classification was incorrect and you would like your image removed please contact me an I will do so immediately.

One of the highlights for me during the recent Besiktas v. Lyon Europa League tie was watching former Gunner Oğuzhan Özyakup. Özyakup never had the chance to break into the first team at Arsenal, but since then has slowly built a very impressive resume for himself at Besiktas in Turkey. It got me thinking about all the promising young players that have come through Arsenal over the past 5-7 years. Each season, Arsenal fans watch them during the preseason tours, and potentially a handful of Carling Cup matches. At the conclusion of each season, a few are released or sold to continue there careers away from Arsenal.

My interest in Özyakup motivated me follow up on a handful of former Gunners. I want to know where they are now, and how they’re playing. As always my approach will be data driven.

And yes, you can consider this article as me outing myself as an Arsenal fan. Watching the French National team during the 2006 World Cup got me into the sport. Zidane, Henry, Ribery, Thuram, Makélélé, Viera, Malouda – amazing side. I remember how different the sport felt compared to the few San Jose Clash games I watched growing up in the 90s. I followed Henry to Arsenal and became a fan during the 2006/2007 season.

Isaac Hayden:

“I like his strengths in the duels… I like his capacity of concentration and I believe as well that technically he is very focused to do well… He is maybe not a creative player but everything he does is intelligent. I like his intelligence and all these qualities together makes me choose him.”

A longtime fixture in the Arsenal youth teams, Isaac Hayden only played a couple Carling Cup matches for Arsenal during the 2013-2014, and 2014-2015 seasons. He then went on loan to the Championship last season with Hull, where he started 9 matches, and came off the bench for another 9. In July of last summer, Arsenal sold Hayden to Newcastle, where he signed a five year deal.

Along with Matthew Palmer, Will Hughes, and Philip Billing, Hayden has established himself as one of the best u-23 central midfielders in the Championship this season. He’s made 27 starts to date for Newcastle, all as a central midfielder (Hayden spent some time as a central defender at Arsenal).

Arsene Wenger was right about his strength in the duel. Hayden has broken up opponents play at an above average rates (adjusted for possession) this season. He is clearly not a pure ball winner, but his tackling, intercepting, and blocking are all about a standard deviation above the mean for a central midfielder. Hayden has also been very strong in the air. He wins headers at very high rates in both the offensive and defensive halves. In addition, his success rates when going for headers are also high.

Hayden’s passing within Newcastle’s system can only be described as ordinary, and conservative. His passing risk (a metric used from my implementation of the expected passing model) is very low, indicating a conservative range of passing. This is confirmed by the rates at which Hayden cycles the ball sideways and sends it backwards. Hayden rarely moves the ball forward, and rarely passes it long. To be fair to him, Newcastle as a team play an above average possession, fairly conservative passing style. Perhaps the team effect is skewing Hayden’s passing profile.

Adjusted for risk (again, using the expected passing model here) Hayden’s passing accuracy is slightly below average. A highly accurate passer when moving the ball laterally, Hayden’s accuracy rates are more troubling when he moves it forward.

Within Newcastle’s passing network, Hayden’s is a fairly influential connective hub. He has played both as the deep man and the middle man in in Newcastle’s 3 man midfield system this season. Hayden’s vertical and laterally touch maps seem to indicate Hayden covers a lot of ground for Newcastle, as well.

Here is Hayden playing a bit deeper than usual in Newcastle’s recent win away to Cardiff (passing network is 11tegen11‘s):

One interesting metric that stands out, especially considering his conservative passing style, is Hayden’s chance creation. His chance creation rate is slightly above the mean for a central midfielder, and his expected assists are a full standard deviation above the mean. Although Hayden plays as a 6, and is a conservative passer of the ball, he is still managing to create chances at surprisingly high rates.

Hayden seems to be adjusting to life after Arsenal very well. I can’t wait to watch how he copes with the Premier League next season.

Thomas Eisfeld:

“He is a Pires type… He appears to be in the box without being noisy and appearing suddenly. When he is there, he finishes well. He has that kind of quality that some midfielders have – not many. They have the timing to get in dangerous situations. When they have those dangerous situations, they are like snakes. They bite you to death because they don’t miss their first touch.”

A year after Arsenal signed Serge Gnabry out of the Stuttgart youth system, Arsenal went back to Germany to sign another promising young attacker – Thomas Eisfeld. Eisfeld came up through the Dortmund youth system before he was allowed to leave for Arsenal in 2012. Like Hayden, Eisfeld only managed a couple appearances in the Carling Cup with the Arsenal first team over the 2012-2013, and 2013-2014 seasons.

In the summer of 2014, Arsenal sold Eisfeld to Fulham. After a successful loan to VFL Bochum, during the 2015-2016 season, Fulham sold Eisfeld to Bochum permanently last summer.

Eisfeld started the 2016-2017 season strong for Bochum, prior to an knee injury that kept him sidelined until April. Even still, Eisfeld’s performances when he has been healthy as a number 10 for Bochum this season are perhaps second only to Greuther Fürth’s Robert Zulj.

Eisfeld’s strength is in his creativity. He creates chances at a very high rate, and expected assists numbers are above average for a number 10, as well. His actual assist total, 2 in around 1100 minutes on the pitch, is lagging far behind his expected assists. In addition to creating chances, Eisfeld has shown indications that Arsene Wenger’s assessment of his play in the box is correct. His expected goal numbers are again, above average for a number 10, and again, are out pacing his actual goal scoring rate.

Here is Eisfeld playing high up the pitch for Bochum in a recent match (passing network is 11tegen11‘s):

For all Eisfeld’s play making, he’s not a influential connector in Bochum’s passing network. Positionally he has played as the number 10 in mostly 3 man midfields for Bochum, and stays fairly high up the pitch. His passing accuracy adjusted for risk is surprisingly average. Like Hayden, albeit from a very different position on the field, Eisfeld is a conservative passer. Eisfeld has a tendency to play the ball backwards, lay it off, and rarely moves it forward. This is not all that surprising given the position he’s playing, and the fact that Bochum like to play with the ball. Eisfeld also wins fouls at a standard deviation above the mean for a number 10.

Eisfeld’s career seems to be back on the upwards trajectory after some stagnant seasons at Arsenal and Fulham. Bochum look set for another season in the Bundesliga 2., so expect Eisfeld to be a major player in that league barring another injury.

Kristoffer Olsson:

The story goes that Liam Brady persuaded Arsene Wenger to sign Kristoffer Olsson from IFK Norrkoping in Sweden in 2011. Over a few seasons in the Arsenal youth system, Olsson only ever featured for Arsenal during one Carling Cup match in the 2013-2014 season. Olsson spent the 2014-2015 season on loan at FC Midtjylland in Denmark before permanently moving there in the summer of 2015.

Olsson established himself as one of the best young midfielders in Denmark along with FC Nordsjaelland’s Stanislav Lobotka, and Brondby’s ball winner Christian Nørgaard. At Midtjylland, Olsson played high up the pitch in 3 man midfields. For how high up the field Olsson played, he intercepted the ball at above average rates, and tackled at average rates for central midfielders. Olsson doesn’t seem to be a defensive liability in the position he plays. Olsson also covered ground at very high rates, moving laterally and vertically.

Olsson’s play is highlighted by his passing accuracy. Adjusted for passing risk, Olsson’s passing accuracy is almost a standard deviation above the mean for central midfielders. In particular, his passing accuracy moving the ball forward in the middle and final thirds is very high. The profile of his passing is riskier than Eisfeld and Hayden, as well. Olsson rarely moved the ball laterally as he either pushed forward, or laid it off. However, Olsson keeps his passing short, and very rarely passed the ball long. In general, Midtjylland played the ball long only slightly below average while Olsson was there.

Although Olsson played at a similar height on the pitch for Midtjylland as Eisfeld does at Bochum, he isn’t as much of a playmaker. His expected assist and expected goal numbers are above average for central midfielders, but not like Eisfeld’s. An interesting outlier in Olsson’s metrics is his complete lack of winning balls aerially. His numbers are so low there perhaps I need to do some data quality checks…

In January, Olsson transferred to AIK in Sweden. It’s still too early there (5 starts) to properly evaluate his performance there.

Oğuzhan Özyakup:

“I’m happy that he came here… He was educated by us and we saw that he had top quality and technically he is very good. Physically he can run all day, he has very good stamina and a good final pass. I always thought he could make a career but at our club he had big competition in front of him and that is why we let him go. It is good to see he has made it to the top level and is now an important player in Turkey.”

Oğuzhan Özyakup is perhaps the most successful former Arsenal youth to establish himself outside of London in recent history. In the summer of 2012, Özyakup moved from Arsenal to Besiktas, and over the course of the past five seasons, has slowly established himself on the European and International stages. Özyakup has been capped 25 times in a talented Turkish midfield, and is a regular starter for one of the most progressive attacking sides in Europe. Besiktas regularly play with over 60 percent of the ball, and Özyakup sits at the heart of their possession as an deep lying attacking hub. Last season, Özyakup and Besiktas won the Turkish Super League – the first Besiktas title since the 2008-2009 season.

While Özyakup has started some matches as a number 10 for Besiktas, his regular position is next to Atiba Hutchinson as one of the deeper lying central midfielders in their 3 man midfield. He has a large influence over their possession as his connectivity within their passing network is strong. Özyakup is a brilliant technical player who’s passing accuracy adjusted for risk is very strong. It’s about a standard deviation above the central midfield mean, and about a standard deviation below elite levels (elite being Santi Cazorla,Toni Kroos, and Andres Iniesta). His accuracy in the middle third moving the ball laterally and forward, and his forward passing in the final third are highlights. Özyakup’s passing risk is average. He moves the ball forward, laterally, and backward at average rates.

Özyakup also rarely gives the ball away due to bad touches or being tackled by opposition, characteristics that are important to Besiktas’ heavy possession style of play.

Here is Özyakup playing next to Hutchinson in a recent win over Caykur Rizespor (passing network is 11tegen11‘s):

In addition to Özyakup’s ability in the build up, his data are strong as a play maker as well. Özyakup plays teammates through very often for a central midfielder, and his expected assist and expected goal numbers are also well above average.

Özyakup’s one weakness may be his defensive contribution. Adjusted for possession he doesn’t break up the opposition’s play at high rates. His tackling rates in particular are below average. Additionally, like Olsson, Özyakup isn’t a threat aerially.

Özyakup’s name is often in the transfer news these days. I am hoping he makes the jump to Spain, Germany, or England this summer.

It seems life after Arsenal for Hayden, Eisfeld, Olsson, and Özyakup has been quite good. All four seem to be on the upwards trajectory in Europe. I look forward to following their careers over the next few seasons.

Please feel free to add any comments/thoughts.

Note – All pass networks are @11tegen11‘s. All other non-sourced images were found using Google Advances Image Search option with image rights set to ‘free to user or share.’If Google’s classification was incorrect and you would like your image removed please contact me an I will do so immediately.

Subjective criticism of Pep Guardiola’s tactics and team selection has been a constant theme this season in the English football media. One of the major issues has been his selection of Claudio Bravo as first choice keeper after he allowed Joe Hart to leave on loan.

After a loss to Leicester this weekend, Alan Shearer gave a subjective opinion:

“Yes [Hart] had not had a great Euros but this was still a top class goalkeeper who was one of the reasons Manchester City won two titles…To get [Man City] to play like Barcelona and Bayern they needed a keeper with a different skill set it seemed, so in came Claudio Bravo. He certainly does have a different skill set, but saving shots does not seem to be part of it. After the last two games there are now even bigger question marks over the signing of Bravo.”

Shearer says savings shots is not part of Bravo’s “skill set,” and implies Hart is top class.

Other writers have tried, and failed, to use statistics to prove their narrative that Hart is a better shot stopper than Bravo. For example, Alaistair Tweedale shows he either has no idea about sample sizes and/or shot quality, or just knows he can get away without using them:

“The biggest worry of all has to be Bravo’s shot-stopping, though. While in Premier League games he has saved 73.7 percent of the shots on target he has faced – which puts him in mid-table among his peers – that rate drops to just 42.9 per cent in the Champions League. That is, he has made only three saves in three European games since moving to City. Hart has kept out 79.2 percent of the shots on target he has faced since moving to Italy.”

Those numbers inform nothing, unfortunately. For one, they don’t take into account location of shot. That is important – not all shots are equal. Also those percentages come from small sample sizes, especially the Champions league rate.

Paul Merson has added into the debate with his opinion of Bravo and Liverpool’s Karius in comparison to Hart:

“What chance do [England] have? It’s crazy, it’s an absolute joke. Our best goalkeeper has to play abroad to get a game. And we have two goalkeepers in the top four who can’t catch a cold.”

Is this narrative true? Who is the better shot stopper, Bravo or Hart?

Methodology:

I went back as far as I could go in my data, to the 2014-2015 season. I looked at 7 different types of save rates for each player across their matches in La Liga, the English Premier League, and Italian Serie A. I am defining a save rate as saves over shot on target including goals.

Results:

For all categories, Bravo has saved shots at a higher rate than Hart over the past 2.5 seasons. However, in only two categories was there a statistically significant difference in save rates between the two keepers.

Based on these results I would say there is some evidence that Bravo is a better shot stopper than Hart at high shots, and shots to his side. I would say there is no evidence that Hart is a better shot stopper in any of these categories.

There doesn’t seem to be evidence to the narrative that Hart is a better shot stopper than Bravo.

Future Extensions:

Given more data, I would go back further in time to increase sample size. Interestingly, Claudio Bravo and Joe Hart have both played in the in a Top 5 European since the 2006-2007 season. Although, Hart only had one start in 2006-2007, and Bravo had no starts in 2007-2008.

There is a downside to going back too far in the data, though. The further we go back in time, the more chance we have to find a statistically significant difference. However, the further we go back in time, the further away we are from answering the question, ‘Who is the better shot stopper today?” I think the 2.5 seasons of data I have used is reasonable, but I at least would like to run the numbers for further look backs.

Note – All images were sourced using Google Advances Image Search option with image rights set to ‘free to user or share.’If Google’s classification was incorrect and you would like your image removed please contact me an I will do so immediately.

As a data analyst some things about football have been difficult for me to understand. For example, what objectively makes a good central midfielder? More so, how do we categorize all the different types of central midfielders?

If you subscribe to today’s football media, you’ll read terms like “central attacking midfielder,” “central midfielder,” and “defensive midfielder”. However, those terms imply physical position on the pitch rather than playing style. Other terms like “Box to Box,” “Deep Lying Playmaker,” and “Anchor Man,” have been introduced as a means to try to imply playing style. For example, the latest installment of SI’s Football Manager includes 27 different assignments for central midfielders.

But what objectively classifies an individual as a “Deep Lying Playmaker”? This is not to say answering this question subjectively is “bad.” However, the opportunity for me as a data analyst is to look at questions like this with an outsider’s perspective.

Rather than try to understand the subjective descriptors that are already popular in the media, I decided to answer the following question as objectively as I could: “What are the different types of central midfielders in Europe?”

My outcome is not perfect. And as always, it is not perfectly objective. Hopefully it provides a different angle in which to view the game. Please feel free to share any comments and/or thoughts you may have.

Methodology

To answer this question I needed data. For those of you who like working with football data, you know all the good stuff is very hard to come by. I can’t afford Opta data, but I do know how to write web-scrapers with python. Without going into too much detail, I used Selenium to build a web-scraper that pulls data from multiple websites. I then wrote more python code using pandas to clean and merge the dataset into a usable format.

I limited my dataset to the last 18 months of player game level data from the Top 5 Leagues in Europe: English Premier League, Spanish La Liga, Italian Serie A, French League 1, and German Bundesliga. I only included matches where a player started as a central midfielder. I only included players in my classification that had played at least the equivalent of 10 matches as a central midfielder (900 minutes).

From there, I decided on 25 features to measure the players on. The features span from attributes (eg. height, weight), to positional information (eg. standard deviation of vertical movement), to passing, defense, and shooting metrics. I have tried to include features that cover the majority of a player’s actions during the match. I have also tried to adjust my features to limit team style effects as much as possible.

However, metric selection is admittedly a subjective process that has its flaws. Team effects can’t be completely nullified and since I am scraping data, I do not have all the information I would like. For example, I don’t have data on player speed. It is fair to say my analysis does not cover every attribute of a central midfielder.

After I had my 25 features, I needed to determine how many different classifications of players I should define. Determining K, or the number of clusters, is a non-standardized task when working with clustering algorithms. Meaning, there are multiple ways to do it.

I frequently use silhouette scores. Simply put, silhouette scores measure how similar an observation is to the rest of its cluster, and different it is to other clusters. I ran silhouette scores 1000 times over my dataset to determine the optimum K value. For my dataset the optimal number of clusters was 20. Meaning, separating the central midfielders of Europe into 20 clusters maximizes the similarity of the players within clusters while minimizing the difference of players in different clusters.

Note, this means I am not optimizing the number of classifications for human readability. Sure it would be easier for our brains to have less categories and for each one to be more discrete. But that’s not what I see in the data.

After I had my K value, I ran the dataset of player features through a kmeans clustering algorithm. The result was 20 distinct clusters of players. Once I had my clusters, I simply compared the attributes of the players in the cluster to the overall population as a means to characterize the clusters.

Classifications

What follows is my classification of 20 distinct types of central midfielders playing in Europe. Some classifications are exciting, some less so. I have included 3-5 example players per classification. In some cases, I have also noted stand out young players (21 and under) who match the classification. Classifications are presented in positional rank from deepest to furthest up the pitch. Because I present the classifications in this way, you may notice some classifications are similar to the previous classification – don’t worry, they are! However, each classification has something that makes it different than the others.

Classification descriptions are meant to characterize the average of each cluster as a whole. Not all descriptors will match each individual player exactly.

All transfer estimate data is not my work and was sourced from transfermarkt.com.

Deep Lying Classifications (8 Types)

Marten De Roon – Deep Lying Ball Winner

Classification 1: Screener – Ultra defensive minded midfielder who sits very deep, often right in front of the defense. Exceptional at breaking up play. Accumulates lots of tackles, but doesn’t win a high percentage of them. Doesn’t get forward up the pitch.

Classification 2: Aerial Ball Winner– Tall defensive minded midfielder who sits deep. Good at winning headers, and breaking up play via intercepting. However, is a below average tackler. Average passer who is rarely knocked off the ball, but offers little to no creativity.

Classification 3: Limited Ball Winner – Strong defensive minded midfielder who sits deep. In contrast to the Aerial Ball Winner, the Limited Ball Winner is exceptional at breaking up play in all relevant categories. However, the Limited Ball Winner is a very poor passer of the ball and offers very little in possession.

Classification 4: Ball Winner – Balling winning midfielder who sits deep but significantly higher than the Screener. Like the Limited Ball Winner, the Ball Winner is exceptional at breaking up play. However, the Ball Winner isn’t as strong physically as the Limited Ball Winner, and is a much better passer.

Classification 5: Defensive Dribbler – Defensive minded midfielder who sits deep. Only above average at breaking up play, but exceptional at recovering loose balls. Doesn’t get forward often but when does is a successful dribbler at playing his team out of trouble. Offers very little in the final third.

Classification 6: Defensive Simple Passer – Strong possession minded defensive midfielder who sits deep. Good at winning headers, but average in breaking up play via tacking and intercepting. Doesn’t get forward often. Good simple passer and rarely turns it over.

Classification 7: Deep Lying Possession Passer – Physically small possession minded midfielder who sits deep. Good short passer and dribbler who rarely turns the ball over. Doesn’t create many chances, or get forward often. Average to below average at breaking up play.

Classification 8: Deep Lying Controller – Exceptional passing midfielder who sits deep. Great passer in all possession passing categories, rarely turns it over, and is also a successful dribbler in the middle third. However, is fairly average at breaking up play. For how great the Controller is at keeping possession, he often has a poor final ball.

Classification 12: – Box to Box Playmaker: Strong central midfielder who covers lots of ground vertically.Good at breaking up play via tacking and intercepting, but unlike other box to box midfielders creates chances at a high rate. Below average ball control, dribbling, and passing.

Classification 13: – Central Ball Playing Midfielder: Physically strong ball playing midfielder who stands his ground in the middle of the pitch. Exceptional passer highlighted by his forward passing. Competent at breaking up play. Only weakness is doesn’t create many chances.

Classification 14: – Central Limited Possession Midfielder: Unspectacular central midfielder who covers a lot of ground laterally. Very good at controlling the ball and avoiding tackles. Usually physically smaller and average at breaking up play.

Classification 15: – Central Possession Midfielder: Central midfielder adept at ball control and passing. Smaller physically than the Ball Playing Midfielder, and not quite as good at passing the ball forward. Average at breaking up play and doesn’t create many chances.

Classification 16: – Central Controller: Exceptional possession midfielder in the center of the field. Very few flaws when it comes to passing and ball control. Unlike the Ball Playing Midfielder however, the Central Controller does not add anything when it comes to breaking up play.

Classification 17: – Advanced Playmaker: Attacking midfielder who sits high up the pitch, but will track back to pick up the ball. Dangerous in the final third as he creates many chances and has an accurate shot. However, the advanced playmaker is tackled often and offers nothing defensively.

Classification 18: – Number 10s: The most dangerous type of midfielder, the Number 10 is second to none at creating chances and passing the ball forward. The 10 sits very high, and is almost a second forward, but will also track back to pick up the ball. Like the advanced playmaker, the 10 doesn’t add much defensively.

Classification 19: – Deep Lying Forwards: Attack minded midfielder that sits very high up the field like the 10s. Exception at creating chances, and an accurate shooter. Adds little else and can be seen almost as a second forward.

Classification 20: – Central Lying Winger: Attacking midfielder who ranges laterally from side to side. Exceptional at creating chances, and actually an accurate tackler. Below average short and forward passer, who fouls opponents often. Almost seems as if these are wingers playing out of position.

Please feel free to add any comments/thoughts about the results or methodology.

Note – All images were sourced using Google Advances Image Search option with image rights set to ‘free to user or share.’If Google’s classification was incorrect and you would like your image removed please contact me an I will do so immediately.